Improved lung cancer classification by employing diverse molecular features of microRNAs

MiRNAs are edited or modified in multiple ways during their biogenesis pathways. It was reported that miRNA editing was deregulated in tumors, suggesting the potential value of miRNA editing in cancer classification. Here we extracted three types of miRNA features from 395 LUAD and control samples, including the abundances of original miRNAs, the abundances of edited miRNAs, and the editing levels of miRNA editing sites. Our results show that eight classification algorithms selected generally had better performances on combined features than on the abundances of miRNAs or editing features of miRNAs alone. One feature selection algorithm, i.e., the DFL algorithm, selected only three features, i.e., the frequencies of hsa-miR-135b-5p, hsa-miR-210-3p and hsa-mir-182_48u (an edited miRNA), from 316 training samples. Seven classification algorithms achieved 100% accuracies on these three features for 79 independent testing samples. These results indicate that the additional information of miRNA editing is useful in improving the classification of LUAD samples. •The editing features of miRNAs are used to construct cancer classification models for the first time.•Three types of molecular features of miRNAs are generated for 5 blocks of LUAD and control samples.•The ComBat-Seq algorithm is used to remove the batch effects of the data sets used.•hsa-miR-135b-5p, hsa-miR-201-3p and hsa-mir-182_48u (an edited miRNA) are selected from the training data set.•Seven algorithms achieve 100% accuracies on the three selected features for independent testing data.